MAPTIME030 / geodata in R
GreenStat - Peter van Horssen
'02 maart, 2018'
Introduction - Who am I
Introduction - What is the Plan ?
geo-data in R
geo-data in R
geo-data in R
geo-data in R
- why do we need this ?
- who needs this ?
r&d for small to midsize (in terms of data) analysis
very usefull for data-exploration (spatial, temporal, attributes)
keep the workflow on one platform
analysis in R, fancy (web)mapping somewhere else
geo-data in R
geo-data in R
Non spatial > spatial data
Non spatial > spatial data
Non spatial > spatial data
Non spatial > spatial data
Non spatial > spatial data
Non spatial > spatial data
Non spatial > spatial data
geo-data in R
import/export external GIS-formats
geo-data in R
geo-data in R
geo-data in R
geo-data in R
library(sf)
library(tidyverse)
library(mapview)
u.buurt.sf %>% mapview()
#u.buurt.sf %>% mapview(zcol="BU_NAAM")
u.buurt.sf %>% mapview(fill=NA)
geo-data in R
Non spatial > data exploration `tidy-style`
Non spatial > data exploration `tidy-style`
df %>%
ggplot(aes(dates,ID)) +
geom_point() +
theme(text = element_text(size = 25))
df %>%
ggplot(aes(maand,var2, group=maand)) +
geom_boxplot()+
theme(text = element_text(size = 25))
Non spatial > data exploration `tidy-style`
df %>% ggplot(aes(var1,var2)) +
geom_boxplot() +
facet_wrap(~maand) + # conditional plot
theme(text = element_text(size = 25))
df %>% ggplot(aes(maand,var2)) +
geom_boxplot() +
facet_wrap(~var1)+
theme(text = element_text(size = 25))
spatial analysis - view selection
spatial analysis - view selection
library(mapview)
library(sf)
library(tidyverse)
# select by spatial object .....!
df.sf[nl01.sf,]
mapview(nl01.sf) + df.sf[nl01.sf,]
#what if ?
# nl01.sf[df.sf,]
#nl01.sf[nl01.sf$NAME_1 =="Noord-Brabant",]
nbr.sf <-
nl01.sf %>% filter(NAME_1 =="Noord-Brabant")
df.sf[nbr.sf,]
mapview( nbr.sf)+ df.sf[nbr.sf,]
# neat !
spatial analysis - buffer
nbr.sf %>% st_crs()
#Coordinate Reference System:
# EPSG: 4326
# proj4string: "+proj=longlat +datum=WGS84 +no_defs"
nbr.sf %>%
st_transform(28992) %>%
# what if we don't do this ?
st_buffer(dist=10000) %>%
mapview() + nbr.sf # what happens here ....?
# st_area ?
# st_length?
spatial analysis - spatial join
df.sf[nl01.sf,] %>%
st_join (nl01.sf %>% select(NAME_1))
#Simple feature collection with 475 features and 6 fields
#Geometry type: POINT
#dimension: XY
#bbox: xmin: 3.52603 ymin: 50.76231 xmax: 7.163211 ymax: 53.48051
#epsg (SRID): 4326
#proj4string: +proj=longlat +datum=WGS84 +no_defs
#First 10 features:
# ID var2 var1 dates m NAME_1 geometry
#2 2 0.4210747 B 2016-02-14 02 Noord-Brabant POINT (4.82705890548415 51....
#3 3 0.3327989 D 2016-04-16 04 Utrecht POINT (5.44536982017802 52....
df.sf[nl01.sf,] %>% #select points IN nl01.sf
st_join (nl01.sf %>% select(NAME_1)) %>% # spatial join & select column 'NAME_1'
mapview(zcol="NAME_1") # display the result
spatial analysis - spatial join & ggplot
library(sf)
library(tidyverse)
df.sf[nl01.sf,] %>%
st_join (nl01.sf %>% select(NAME_1)) %>%
ggplot(aes(NAME_1,var2)) + geom_boxplot() +
theme(axis.text.x = element_text(angle = 60, hjust = 1))+
theme(text = element_text(size = 15))
spatial analysis - puzzle !
